Authors:
L. Palmerini
1
;
L. Rocchi
1
;
S. Mellone
1
;
L. Chiari
1
and
F. Valzania
2
Affiliations:
1
University of Bologna, Italy
;
2
University of Modena and Reggio Emilia, Italy
Keyword(s):
Feature Selection, Parkinson’s Disease, Accelerometer.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
BioInformatics & Pattern Discovery
;
Data Reduction and Quality Assessment
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Mining High-Dimensional Data
;
Symbolic Systems
Abstract:
The Timed Up and Go (TUG) is a widely used clinical test to assess mobility and fall risk in Parkinson’s disease (PD). The traditional outcome of this test is its duration. Since this single measure cannot provide insight on subtle differences in test performances, we considered an instrumented TUG (iTUG). The aim was to find, by means of a feature selection, the best set of quantitative measures that would allow an objective evaluation of gait function in PD. We instrumented the TUG using a triaxial accelerometer. Twenty early-mild PD and twenty age-matched control subjects performed normal and dual task TUG trials. Several temporal, coordination and smoothness measures were extracted from the acceleration signals; a wrapper feature selection was implemented for different classifiers with an exhaustive search for subsets from 1 to 3 features. A leave-one-out cross validation (LOOCV) was implemented both for the feature selection and for the evaluation of the classifier, resulting in
a nested LOOCV. The resulting selected features permit to obtain a good accuracy (7.5% of misclassification rate) in the classification of PD. Interestingly the traditional TUG duration was not selected in any of the best subsets.
(More)